Issue 12, 2021

Data-efficient machine learning for molecular crystal structure prediction

Abstract

The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level—for which an efficient implementation is available—with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP.

Graphical abstract: Data-efficient machine learning for molecular crystal structure prediction

Supplementary files

Article information

Article type
Edge Article
Submitted
19 okt 2020
Accepted
05 fev 2021
First published
11 fev 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2021,12, 4536-4546

Data-efficient machine learning for molecular crystal structure prediction

S. Wengert, G. Csányi, K. Reuter and J. T. Margraf, Chem. Sci., 2021, 12, 4536 DOI: 10.1039/D0SC05765G

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